Objective of this study is to develop simulation for predicting mechanical properties of Ti-6Al-4V alloy. Rockwell Hardness (HRC), Ultimate tensile strength (UTS) and elongation (ε) are predicted by using Neural Network (NN) with multilayer feedforward architecture. The input of simulations are chemical compositions of Ti-alloy at room temperature. The data of the mechanical properties which are reported by other researchers are used for the NN training and Gradient Descent (GD) and Lavenberg Marquardt (LM) are applied as methods of learning algorithms. The results of training by both methods are compared in order to obtain high performance of output criteria which are determined by a Normalized Root Mean Square Error (NRMSE). is used to determine the performance of output criteria. In training, the NRMSE output calculated by GD algorithm show that HRC, UTS and ε are 0.024, 0.0717 and 0.1375 respectively, while LM algorithm for HRC, UTS and ε are 0.0207, 0.0689 and 0.1150, respectively. The NRMSE predicted output of GD algorithm for HRC, UTS, and ε are 0.0658, 0.0338 and 0.2994, while LM algorithm for HRC, UTS and ε are 0.0371, 0.1192 and 0.5487 respectively. In training, values of NRMSE calculated by LM algorithm is smaller than GD algorithm. These results suggest that LM algorithm shows excellent ability for training, however the GD method is more appropriate for the training algorithm in order to obtain a high performance of output criteria. It can be concluded that the NN can be applied for predicting mechanical properties of Ti-6Al-4V alloys.